Markov Decision Processes with Applications in Wireless Sensor Networks: A Survey

Wireless sensor networks (WSNs) consist of autonomous and resource-limited devices. The devices cooperate to monitor one or more physical phenomena within an area of interest. WSNs operate as stochastic systems because of randomness in the monitored environments. For long service time and low maintenance cost, WSNs require adaptive and robust methods to address data exchange, topology formulation, resource and power optimization, sensing coverage and object detection, and security challenges. In these problems, sensor nodes are to make optimized decisions from a set of accessible strategies to achieve design goals. This survey reviews numerous applications of the Markov decision process (MDP) framework, a powerful decision-making tool to develop adaptive algorithms and protocols for WSNs. Furthermore, various solution methods are discussed and compared to serve as a guide for using MDPs in WSNs

A critical analysis of research potential, challenges and future directives in industrial wireless sensor networks

In recent years, Industrial Wireless Sensor Networks (IWSNs) have emerged as an important research theme with applications spanning a wide range of industries including automation, monitoring, process control, feedback systems and automotive. Wide scope of IWSNs applications ranging from small production units, large oil and gas industries to nuclear fission control, enables a fast-paced research in this field. Though IWSNs offer advantages of low cost, flexibility, scalability, self-healing, easy deployment and reformation, yet they pose certain limitations on available potential and introduce challenges on multiple fronts due to their susceptibility to highly complex and uncertain industrial environments. In this paper a detailed discussion on design objectives, challenges and solutions, for IWSNs, are presented. A careful evaluation of industrial systems, deadlines and possible hazards in industrial atmosphere are discussed. The paper also presents a thorough review of the existing standards and industrial protocols and gives a critical evaluation of potential of these standards and protocols along with a detailed discussion on available hardware platforms, specific industrial energy harvesting techniques and their capabilities. The paper lists main service providers for IWSNs solutions and gives insight of future trends and research gaps in the field of IWSNs

Mitigating the Event and Effect of Energy Holes in Multi-hop Wireless Sensor Networks Using an Ultra-Low Power Wake-up Receiver and an Energy Scheduling Technique

This research work presents an algorithm for extending network lifetime in multi-hop wireless sensor networks (WSN). WSNs face energy gap issues around sink nodes due to the transmission of large amounts of data through nearby sensor nodes. The limited power supply to the nodes limits the lifetime of the network, which makes energy efficiency crucial. Multi-hop communication has been proposed as an efficient strategy, but its power consumption remains a research challenge. In this study, an algorithm is developed to mitigate energy holes around the sink nodes by using a modified ultra-low-power wake-up receiver and an energy scheduling technique. Efficient power scheduling reduces the power consumption of the relay node, and when the residual power of the sensor node falls below a defined threshold, the power emitters charge the nodes to eliminate energy-hole problems. The modified wake-up receiver improves sensor sensitivity while staying within the micro-power budget. This study's simulations showed that the developed RF energy harvesting algorithm outperformed previous work, achieving a 30% improvement in average charged energy (AEC), a 0.41% improvement in average energy (AEH), an 8.39% improvement in the number of energy transmitters, an 8.59% improvement in throughput, and a 0.19 decrease in outage probability compared to the existing network lifetime enhancement of multi-hop wireless sensor networks by RF Energy Harvesting algorithm. Overall, the enhanced power efficiency technique significantly improves the performance of WSNs

Recent advances in wireless sensor networks with environmental energy harvesting

Shu, L.; Liao, W.; Lloret, J.; Wang, L. (2016). Recent advances in wireless sensor networks with environmental energy harvesting. International Journal of Sensor Networks. 21(4):205-207. http://hdl.handle.net/10251/18736720520721

Low-Power Pıc-Based Sensor Node Devıce Desıgn And Theoretıcal Analysıs Of Energy Consumptıon In Wıreless Sensor Networks

Teknolojinin ilerlemesi, daha enerji verimli ve daha ucuz elektronik bileşenlerinin daha küçük üretilmesini sağlamıştır. Bu nedenle, daha önce mevcut birçok bilgisayar ve elektronik bilim-mühendislik fikirleri uygulanabilir hale gelmiştir. Bunlardan birisi de kablosuz sensör ağları teknolojisidir. Kablosuz algılayıcı ağlar, düşük enerji tüketimi ve gerekli teknik gereksinimlerin gerçekleşmesi ile uygulanabilir hale gelmiştir. Ayrıca, Kablosuz algılayıcı ağlarının tasarımında iletişim algoritmaları, enerji tasarruf protokolleri ve yenilenebilir enerji teknolojileri gibi diğer bilimsel çalışmalar zorunlu hale gelmiştir. Bu tez, mikroelektronik sistemler, kablosuz iletişim ve dijital elektronik teknolojisinin ilerlemesiyle uygulanabilir hale gelmiş sensör ağları teknolojisini kapsamaktadır. Birincisi, algılama görevleri ve potansiyel algılayıcı ağ uygulamaları araştırılmış ve algılayıcı ağlarının tasarımını etkileyen faktörlerin gözden geçirilmesi sağlanmıştır. Ardından sensör ağları için iletişim mimarisi ana hatlarıyla belirtilmiştir. Ayrıca, tek bir düğümün WLAN ile iletişim kurabilmesi için yeni donanım mimarisi tasarlanmış ve düğümlerde yenilenebilir enerji kaynakları kullanılmıştır. Bu tezde WSN, analitik bilim ve uygulamalı bilim açısından incelenmiştir. Düşük enerji tüketimi ve iletişim protokolleri arasındaki ilişki değerlendirilmiş ve bilimsel sonuçlara varılmıştır. Teorik analizler bilimsel uygulamalarla desteklenmiştir. Çalışmalar, düşük enerji ve maksimum verimlilik prensibinin gerçekleştirilmesine dayalı kablosuz sensör ağları üzerinde gerçekleştirilmiştir. Kablosuz sensör ağlari sistemi tasarlandıktan sonra; sensör düğümlerinin enerji tüketimi ve kablosuz ağdaki davranışları test ve analiz edilmiştir. Düşük enerji tüketimi ile sensör düğümleri arasındaki ilişki detaylı olarak değerlendirilmiştir. PIC Tabanlı mikro denetleyiciler sensör düğümlerinin tasarımında kullanılmış ve çok düşük maliyetli tasarım için ultra düşük güçte, nanoWatt teknolojisi ile desteklenen sensör düğümleri tasarlanmıştır. İşleme birimi, bellek birimi ve kablosuz iletişim birimi sensör viii düğümlerine entegre edilmiştir. Tasarlanan sensör düğümünün işletim sistemi PIC C dili ile yazılmıştır ve PIC işletim sistemi nem, sıcaklık, ışığa duyarlılık ve duman sensörü gibi farklı özelliklerin ölçülmesine izin vermiştir. Sensörlerden gelen verilerin merkezi bir konumdan kaydedilmesi ve izlenebilmesi için, C# programlama dili ile bilgisayar yazılımı geliştirilmiştir. Gelişmiş algılayıcı düğümler tarafından alınan kararların uygulanması için yazılım algoritması ve donanım modüllerini içeren karar verme sistemi tasarlanmıştır. Gelişmiş PIC Tabanlı sensör düğümleri, enerji üretimi ve enerji tasarrufu için, güneş enerjisi paneli, şarj edilebilir pil ve süper kapasitör gibi yenilenebilir enerji kaynakları ile benzersiz bir PIC Kontrollü voltaj birimi ile desteklenmiştir. Geliştirilmiş kablosuz sensör ağları sistemi, endüstri uygulamaları, akıllı fabrikalar ve akıllı evler gibi günlük hayat uygulamaları için de kullanılabilecektir. Kablosuz algılayıcı ağlar geniş bir aralıkta kullanılmak üzere tasarlanmıştır. Tezin sonuçları, özellikle yenilenebilir enerji kaynakları ile WSN'nin geliştirilmesine yardımcı olmayı amaçlamaktadır

QoS-Aware Energy Management and Node Scheduling Schemes for Sensor Network-Based Surveillance Applications

Recent advances in wireless technologies have led to an increased deployment of Wireless Sensor Networks (WSNs) for a plethora of diverse surveillance applications such as health, military, and environmental. However, sensor nodes in WSNs usually suffer from short device lifetime due to severe energy constraints and therefore, cannot guarantee to meet the Quality of Service (QoS) needs of various applications. This is proving to be a major hindrance to the widespread adoption of WSNs for such applications. Therefore, to extend the lifetime of WSNs, it is critical to optimize the energy usage in sensor nodes that are often deployed in remote and hostile terrains. To this effect, several energy management schemes have been proposed recently. Node scheduling is one such strategy that can prolong the lifetime of WSNs and also helps to balance the workload among the sensor nodes. In this article, we discuss on the energy management techniques of WSN with a particular emphasis on node scheduling and propose an energy management life-cycle model and an energy conservation pyramid to extend the network lifetime of WSNs. We have provided a detailed classification and evaluation of various node scheduling schemes in terms of their ability to fulfill essential QoS requirements, namely coverage, connectivity, fault tolerance, and security. We considered essential design issues such as network type, deployment pattern, sensing model in the classification process. Furthermore, we have discussed the operational characteristics of schemes with their related merits and demerits. We have compared the efficacy of a few well known graph-based scheduling schemes with suitable performance analysis graph. Finally, we study challenges in designing and implementing node scheduling schemes from a QoS perspective and outline open research problems

QoS BASED ENERGY EFFICIENT ROUTING IN WIRELESS SENSOR NETWORK

A Wireless Sensor Networks (WSN) is composed of a large number of low-powered sensor nodes that are randomly deployed to collect environmental data. In a WSN, because of energy scarceness, energy efficient gathering of sensed information is one of the most critical issues. Thus, most of the WSN routing protocols found in the literature have considered energy awareness as a key design issue. Factors like throughput, latency and delay are not considered as critical issues in these protocols. However, emerging WSN applications that involve multimedia and imagining sensors require end-to-end delay within acceptable limits. Hence, in addition to energy efficiency, the parameters (delay, packet loss ratio, throughput and coverage) have now become issues of primary concern. Such performance metrics are usually referred to as the Quality of Service (QoS) in communication systems. Therefore, to have efficient use of a sensor node’s energy, and the ability to transmit the imaging and multimedia data in a timely manner, requires both a QoS based and energy efficient routing protocol. In this research work, a QoS based energy efficient routing protocol for WSN is proposed. To achieve QoS based energy efficient routing, three protocols are proposed, namely the QoS based Energy Efficient Clustering (QoSEC) for a WSN, the QoS based Energy Efficient Sleep/Wake Scheduling (QoSES) for a WSN, and the QoS based Energy Efficient Mobile Sink (QoSEM) based Routing for a Clustered WSN. Firstly, in the QoSEC, to achieve energy efficiency and to prolong network/coverage lifetime, some nodes with additional energy resources, termed as super-nodes, in addition to normal capability nodes, are deployed. Multi-hierarchy clustering is done by having super-nodes (acting as a local sink) at the top tier, cluster head (normal node) at the middle tier, and cluster member (normal node) at the lowest tier in the hierarchy. Clustering within normal sensor nodes is done by optimizing the network/coverage lifetime through a cluster-head-selection algorithm and a sleep/wake scheduling algorithm. QoSEC resolves the hot spot problem and prolongs network/coverage lifetime. Secondly, the QoSES addressed the delay-minimization problem in sleep/wake scheduling for event-driven sensor networks for delay-sensitive applications. For this purpose, QoSES assigns different sleep/wake intervals (longer wake interval) to potential overloaded nodes, according to their varied traffic load requirement defined a) by node position in the network, b) by node topological importance, and c) by handling burst traffic in the proximity of the event occurrence node. Using these heuristics, QoSES minimizes the congestion at nodes having heavy traffic loads and ultimately reduces end-to-end delay while maximizing the throughput. Lastly, the QoSEM addresses hot spot problem, delay minimization, and QoS assurance. To address hot-spot problem, mobile sink is used, that move in the network to gather data by virtue of which nodes near to the mobile sink changes with each movement, consequently hot spot problem is minimized. To achieve delay minimization, static sink is used in addition to the mobile sink. Delay sensitive data is forwarded to the static sink, while the delay tolerant data is sent through the mobile sink. For QoS assurance, incoming traffic is divided into different traffic classes and each traffic class is assigned different priority based on their QoS requirement (bandwidth, delay) determine by its message type and content. Furthermore, to minimize delay in mobile sink data gathering, the mobile sink is moved throughout the network based on the priority messages at the nodes. Using these heuristics, QoSEM incur less end-to-end delay, is energy efficient, as well as being able to ensure QoS. Simulations are carried out to evaluate the performance of the proposed protocols of QoSEC, QoSES and QoSEM, by comparing their performance with the established contemporary protocols. Simulation results have demonstrated that when compared with contemporary protocols, each of the proposed protocol significantly prolong the network and coverage lifetime, as well as improve the other QoS routing parameters, such as delay, packet loss ratio, and throughput

Enabling Hardware Green Internet of Things: A review of Substantial Issues

Between now and the near future, the Internet of Things (IoT) will redesign the socio-ecological morphology of the human terrain. The IoT ecosystem deploys diverse sensor platforms connecting millions of heterogeneous objects through the Internet. Irrespective of sensor functionality, most sensors are low energy consumption devices and are designed to transmit sporadically or continuously. However, when we consider the millions of connected sensors powering various user applications, their energy efficiency (EE) becomes a critical issue. Therefore, the importance of EE in IoT technology, as well as the development of EE solutions for sustainable IoT technology, cannot be overemphasised. Propelled by this need, EE proposals are expected to address the EE issues in the IoT context. Consequently, many developments continue to emerge, and the need to highlight them to provide clear insights to researchers on eco-sustainable and green IoT technologies becomes a crucial task. To pursue a clear vision of green IoT, this study aims to present the current state-of-the art insights into energy saving practices and strategies on green IoT. The major contribution of this study includes reviews and discussions of substantial issues in the enabling of hardware green IoT, such as green machine to machine, green wireless sensor networks, green radio frequency identification, green microcontroller units, integrated circuits and processors. This review will contribute significantly towards the future implementation of green and eco-sustainable IoT

An Energy Model Using Sleeping Algorithms for Wireless Sensor Networks under Proactive and Reactive Protocols: A Performance Evaluation

The continuous evolution of the Internet of Things (IoT) makes it possible to connect everyday objects to networks in order to monitor physical and environmental conditions, which is made possible due to wireless sensor networks (WSN) that enable the transfer of data. However, it has also brought about many challenges that need to be addressed, such as excess energy consumption. Accordingly, this paper presents and analyzes wireless network energy models using five different communication protocols: Ad Hoc On-Demand Distance Vector (AODV), Multi-Parent Hierarchical (MPH), Dynamic Source Routing (DSR), Low Energy Adaptive Clustering Hierarchy (LEACH) and Zigbee Tree Routing (ZTR). First, a series of metrics are defined to establish a comparison and determine which protocol exhibits the best energy consumption performance. Then, simulations are performed and the results are compared with real scenarios. The energy analysis is conducted with three proposed sleeping algorithms: Modified Sleeping Crown (MSC), Timer Sleeping Algorithm (TSA), and Local Energy Information (LEI). Thereafter, the proposed algorithms are compared by virtue of two widely used wireless technologies, namely Zigbee and WiFi. Indeed, the results suggest that Zigbee has a better energy performance than WiFi, but less redundancy in the topology links, and this study favors the analysis with the simulation of protocols with different nature. The tested scenario is implemented into a university campus to show a real network running

Markov decision processes with applications in wireless sensor networks: A survey

Ministry of Education, Singapore under its Academic Research Funding Tier